SoroushVahidi/scidocs huggingface dataset:
pypet: A Python Toolkit for Data Management of Parameter Explorations
(2016)

pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
Foreground object detection from videos containing complex background
(2003)

This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.
Amorphous silicon thin film transistor circuit integration for organic LED displays on glass and plastic
(2004)

This paper presents design considerations along with measurement results pertinent to hydrogenated amorphous silicon (a-Si:H) thin film transistor (TFT) drive circuits for active matrix organic light emitting diode (AMOLED) displays. We describe both pixel architectures and TFT circuit topologies that are amenable for vertically integrated, high aperture ratio pixels. Here, the OLED layer is integrated directly above the TFT circuit layer, to provide an active pixel area that is at least 90% of the total pixel area with an aperture ratio that remains virtually independent of scaling. Both voltage-programmed and current-programmed drive circuits are considered. The latter provides compensation for shifts in device characteristics due to metastable shifts in the threshold voltage of the TFT. Various drive circuits on glass and plastic were fabricated and tested. Integration of on-panel gate drivers is also discussed where we present the architecture of an a-Si:H based gate de-multiplexer that is threshold voltage shift invariant. In addition, a programmable current mirror with good linearity and stability is presented. Programmable current sources are an essential requirement in the design of source driver output stages.
Nonreciprocal Phase-Shift Composite Right/Left Handed Microstrip Lines Using Ferrite-Rod-Embedded Substrate
(2009)

Nonreciprocal phase-shift composite right/left handed transmission lines with small nonreciprocity in the amplitude of the transmission coefficients are proposed. The present structures are composed of a microstrip line periodically loaded with series capacitance and shunt inductance, and a ferrite rectangular rod is embedded in the dielectric substrate just below the center strip. The balanced composite right/left handed transmission line with nonreciprocal phase characteristics is designed. The numerical simulation results validate that the nonreciprocity in magnitude of the transmission coefficients is negligibly small whereas the nonreciprocity in the phase is still kept relatively large for the present configuration.
EXPLORING THE CONSUMERS ’ ACCEPTANCE OF ELECTRONIC RETAILING USING TECHNOLOGY ACCEPTANCE MODEL
(2015)

Following the approach of Pavlou (2003), we investigate key drivers for consumers̀ electronic retailing (e-tailing) acceptance that are integrated into TAM (technology acceptance model). TAM is one of the better known models for explaining the intention to use technology and it integrates two perceptions: usefulness and ease of use. Th e main purpose of this paper is to explore the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers. In particular, the focus of the paper is on the areas which infl uenced customer commitment and loyalty in e-tailing. Th e results of the quantitative study among Croatian consumers show that electronic retailing, e.g. purchasing via the Internet, would enhance consumers̀ eff ectiveness in getting product information and their eff ectiveness in purchasing products.
Large-scale learning of word relatedness with constraints
(2012)

Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.
The Use of Information Retrieval Techniques for Intrusion Detection
(1997)

Intrusion detection is a broad problem, and we need a greater range of tools than is currently available. In this article, we report a new approach. We have applied information retrieval techniques to index audit trails. These indexes can be extremely e cient at detecting attacks whose signature is an unusual combination of events, and they may consume only a very small additional amount of storage. This approach allows the intrusion detection community to adopt a wide range of techniques developed in applications ranging from library science to web search engines.
Image-based ghostings for single layer occlusions in augmented reality
(2010)

In augmented reality displays, X-Ray visualization techniques make hidden objects visible through combining the physical view with an artificial rendering of the hidden information. An important step in X-Ray visualization is to decide which parts of the physical scene should be kept and which should be replaced by overlays. The combination should provide users with essential perceptual cues to understand the relationship of depth between hidden information and the physical scene. In this paper we present an approach that addresses this decision in unknown environments by analyzing camera images of the physical scene and using the extracted information for occlusion management. Pixels are grouped into perceptually coherent image regions and a set of parameters is determined for each region. The parameters change the X-Ray visualization for either preserving existing structures or generating synthetic structures. Finally, users can customize the overall opacity of foreground regions to adapt the visualization.
Twin gate rectangular recessed channel (TG-RRC) MOSFET for digital-logic applications
(2017)

In this paper, twin gate rectangular recessed channel (TG-RRC) MOSFET with independent gate control is used to realize its application in digital electronics by using it as two input logic. The input logic is controlled by the independent gates which have different work functions (Φ1 for gate 1 and Φ2 gate 2) which are separated by oxide layer of 2 nm, thus controlling various electrical parameters such as current density, potential etc. across the channel in a swift manner and also increasing the switching characteristics of the device. It can give the full functionality of “AND” and “NAND” gate. We have shown the structure and functioning of the gates with a simulation on Silvaco TCAD. Since it is one of the fundamental gates has much applicability in digital logics starting from basic Flip-flops to sequential circuits in SRAM or DRAM cells.
Robot path planning : an object-oriented approach
(2004)

Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. For planning of pick-and-place tasks it is necessary to choose a grasp suitable to the task. Unless the grasp is given, some sort of grasp planning has to be performed. This thesis presents a fast grasp planner for a threefingered robot hand. The grasp planner could be used in an industrial setting, where a robot is to pick up irregularly shaped objects from a conveyor belt. In conjunction with grasp planning, a new method for evaluating grasp stability is presented.
A virtual reality based exercise system for hand rehabilitation post-stroke: transfer to function
(2004)

This paper presents preliminary results from a virtual reality (VR)-based system for hand rehabilitation that uses a CyberGlove and a Rutgers Master II-ND haptic glove. This computerized system trains finger range of motion, finger flexion speed, independence of finger motion, and finger strength using specific VR simulation exercises. A remote Web-based monitoring station was developed to allow telerehabilitation interventions. The remote therapist observes simplified versions of the patient exercises that are updated in real time. Patient data is stored transparently in an Oracle database, which is also Web accessible through a portal GUI. Thus the remote therapist or attending physician can graph exercise outcomes and thus evaluate patient outcomes at a distance. Data from the VR simulations is complemented by clinical measurements of hand function and strength. Eight chronic post-stroke subjects participated in a pilot study of the above system. In keeping with variability in both their lesion size and site and in their initial upper extremity function, each subject showed improvement on a unique combination of movement parameters in VR training. Importantly, these improvements transferred to gains on clinical tests, as well as to significant reductions in task-completion times for the prehension of real objects. These results are indicative of the potential feasibility of this exercise system for rehabilitation in patients with hand dysfunction resulting from neurological impairment.
Research challenges in legal-rule and QoS-aware cloud service brokerage
(2018)

The ICT industry and specifically critical sectors, such as healthcare, transportation, energy and government, require as mandatory the compliance of ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control that customers have on the services. Today, the cloud industry is aware of this problem (as evidenced by the compliance program of many cloud service providers), and the research community is addressing the many facets of the legal-rule compliance checking and quality assurance problem. Cloud service brokerage plays an important role in legislation compliance and QoS management of cloud services. In this paper we discuss our experience in designing a legal-rule and QoS-aware cloud service broker, and we explore relate research issues. Specifically we provide three main contributions to the literature: first, we describe the detailed design architecture of the legal-rule and QoS-aware broker. Second, we discuss our design choices which rely on the state of the art solutions available in literature. We cover four main research areas: cloud broker service deployment, seamless cloud service migration, cloud service monitoring, and legal rule compliance checking. Finally, from the literature review in these research areas, we identify and discuss research challenges.
Arc Flash Calculations for a 1.3-MW Photovoltaic System
(2015)

When planning for the installation of a 1.3-MW photovoltaic (PV) system and its integration into an existing facility's electrical system, an arc flash study was performed to determine the best protective device settings to minimize the arc flash energy in the PV system and to determine the effect of the additional short-circuit current on arc flash calculations for the existing system. The existing system has distribution at 13.8 kV and two 10-MVA transformers to a 2.4-kV feeder system. The PV system adds the hazard of 500-Vdc subarray collection wiring and terminations. The dc arc flash exposure of PV systems, including those in the inverter enclosures, can be complicated, and this paper outlines the basic data collected for the study, the steps taken to complete the calculations, the methodology used, the results, and the team's learnings.
Joint Optimization of Constellation With Mapping Matrix for SCMA Codebook Design
(2017)

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.
Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks
(2018)

Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token’s functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening
(2017)

We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.
Ako: Decentralised Deep Learning with Partial Gradient Exchange
(2016)

Distributed systems for the training of deep neural networks (DNNs) with large amounts of data have vastly improved the accuracy of machine learning models for image and speech recognition. DNN systems scale to large cluster deployments by having worker nodes train many model replicas in parallel; to ensure model convergence, parameter servers periodically synchronise the replicas. This raises the challenge of how to split resources between workers and parameter servers so that the cluster CPU and network resources are fully utilised without introducing bottlenecks. In practice, this requires manual tuning for each model configuration or hardware type.
 We describe Ako, a decentralised dataflow-based DNN system without parameter servers that is designed to saturate cluster resources. All nodes execute workers that fully use the CPU resources to update model replicas. To synchronise replicas as often as possible subject to the available network bandwidth, workers exchange partitioned gradient updates directly with each other. The number of partitions is chosen so that the used network bandwidth remains constant, independently of cluster size. Since workers eventually receive all gradient partitions after several rounds, convergence is unaffected. For the ImageNet benchmark on a 64-node cluster, Ako does not require any resource allocation decisions, yet converges faster than deployments with parameter servers.
Positional asphyxia or diabetic ketoacidosis? A case report.
(2011)

We describe an autopsy case in which a patient with diabetic ketoacidosis (DKA) was found in a head-down position. A female in her late 70s was found dead in her home in a supine position on the kitchen floor. The upper part of her body was hanging down over the edge of the kitchen floor to the backyard through the open window. External examination revealed congestion of the head and upper region of the face and neck. There were numerous petechiae on the superior palpebral conjunctivae and upper part of the oral mucosa. On internal examination, extensive hemorrhages in the subcutaneous fat tissues and muscles were observed at the upper part of the neck, although there were no external injuries on the neck. Histopathological examination revealed that hemorrhages were accompanied with infiltration of polymorphonuclear leukocytes both within and around the hemorrhages on the neck skin. Nodular glomerulosclerosis and many fat droplets in the cytoplasm of proximal tubule cells were found in the kidney. Postmortem blood analysis showed acetone (204.2 μg/ml), HbA1c (10.8%), acetoacetate (<2.0 μmol/l), 3-hydroxybutyrate (11,844 μmol/l), blood urea nitrogen (128.9 mg/dl), and creatinine (3.11 mg/dl). The glucose and acetone levels in the urine were 876.7 mg/dl and 201.4 μg/ml, respectively, suggesting that she suffered severe DKA. However, since hemorrhage of the neck could have developed only when she was still alive, asphyxia should have arisen antemortem. Based on these findings, we concluded that the direct cause of her death is positional asphyxia, which was resulted from DKA. It is difficult to diagnose the cause of death when the victim is in an unusual posture. To confirm a suspicion of positional asphyxia, photographs of the undisturbed scene are useful in addition to a precise autopsy and accurate examinations.
The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick
(2008)

In this paper we investigate the impact of staff turnover on software projects. In particular we investigate whether high staff turnover damages project success. We analyse data from an empirical study of 89 software practitioners to show that projects with high staff turnover are less successful. Furthermore our empirical data suggests a relationship between high staff turnover on projects and low staff motivation levels. We discuss factors which have been previously found to improve motivation levels and conclude that improving motivation levels can reduce staff turnover, which in turn increases project success.
Fast Global Registration
(2016)

We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.